Method and system for suppressing ultra-wideband ranging random noise

Abstract

The invention discloses a method and a system for suppressing ultra-wideband ranging random noise, and relates to the field of ultra-wideband ranging. The problem that in the ultra-wideband positioning process, the ultra-wideband ranging value fluctuates greatly after being affected by the environment is solved. The method comprises the following steps: acquiring original data of ultra-wideband ranging; variational mode decomposition is carried out on the original data of the ultra wide band distance measurement; and processing the decomposed data by adopting a wavelet threshold denoising method to obtain smooth ultra-wideband ranging data. The method is applied to the field of indoor positioning.

Description

Technical Field

[0001] The invention relates to the field of ultra-wideband ranging, and in particular to a method for suppressing random noise in ultra-wideband ranging. Background Art

[0002] With the rapid development of social economy in recent years, indoor environment has become an important part of people's daily life and work. According to relevant research, people spend 80% of their time indoors in a day. And as people's demand for life increases, the indoor environment gradually tends to be diversified and complex. Therefore, the significance and value of indoor positioning services are increasing day by day.

[0003] At present, in the field of indoor positioning, common means include: WIFI, Zigbee, RFID, BLE, UWB and other active sensing devices. However, in indoor positioning, the above methods are limited by the positioning principle and have certain shortcomings. The positioning method based on WIFI has a large coverage range, but the positioning accuracy is 1-5m, and it is seriously affected by the environment; the positioning method based on Zigbee has a small coverage range and a positioning accuracy of about 1-3m; the positioning method based on RFID is a passive positioning method, but its positioning range is very small, and dense readers and writers are required, and its positioning accuracy is about 1m; the positioning method based on BLE has the advantage of low power consumption, but its positioning accuracy is only about 1-3m, and it is easily affected by the environment. Compared with the above methods, the positioning method based on UWB not only has low power consumption and low latency performance, but also has good anti-multipath performance and high-precision positioning performance, with a positioning accuracy of about 0.1m. Therefore, ultra-wideband indoor positioning technology has been widely used. However, in the process of ultra-wideband positioning, the ultra-wideband ranging value will fluctuate greatly due to the influence of the environment. This directly affects the indoor position solution, which will increase the positioning error. Moreover, when the fluctuation is serious, the solution may not converge, resulting in positioning failure. Summary of the invention

[0004] In view of the problem that the ultra-wideband ranging value may fluctuate greatly after being affected by the environment during ultra-wideband positioning, the present invention proposes a method for suppressing the random noise of ultra-wideband ranging, the method comprising:

[0005] S1: Get the raw data of ultra-wideband ranging;

[0006] S2: performing variational mode decomposition on the raw data of the ultra-wideband ranging;

[0007] S3: The decomposed data is processed using the wavelet threshold denoising method to obtain smooth ultra-wideband ranging data.

[0008] Furthermore, a preferred method is proposed, wherein step S2 comprises:

[0009] S21: Assume that the spectrum of each mode is concentrated near the center frequency of the mode;

[0010] S22: Perform Hilbert transform on the single-sided spectrum of each mode in the raw data of ultra-wideband ranging to extract the instantaneous amplitude and phase;

[0011] S23: Use the exponential term to shift the spectrum of the mode to the baseband;

[0012] S24: Estimate the bandwidth of the baseband signal by Gaussian smoothing;

[0013] S25: Construct the optimization objective function and solve it using the augmented Lagrange multiplier method;

[0014] S26: using an alternating direction multiplier algorithm to iteratively solve each mode and its corresponding center frequency until a preset convergence condition is met, thereby obtaining a plurality of independent eigenmode functions.

[0015] Furthermore, a preferred method is proposed, wherein step S25 comprises:

[0016]

[0017] Among them, {u k (t)}={u 1 (t),L,u K (t)} is the mode, {ω k}={ω 1 ,L,ω K} is the center frequency corresponding to the mode, α is the penalty factor of the quadratic term, λ is the Lagrangian operator, K is the total number of modes, δ(t) is the Dirac function, x(t) is the original signal, j is the imaginary unit, ω K is the center frequency of the kth modal component, and t is the time variable.

[0018] Furthermore, a preferred method is proposed, wherein step S3 comprises:

[0019] S31: Select an appropriate wavelet basis and decomposition layer number to perform wavelet decomposition on each intrinsic mode function to obtain wavelet coefficients;

[0020] S32: selecting a suitable threshold according to the noise characteristics of the original signal, and processing the wavelet coefficients at each scale according to the threshold;

[0021] S33: Perform inverse wavelet transform on the processed wavelet coefficients to reconstruct the denoised signal.

[0022] Furthermore, a preferred embodiment is proposed, in which the wavelet basis in step S31 is the Db6 wavelet in the Daubechies wavelet.

[0023] Furthermore, a preferred method is proposed, in which the number of decomposition layers in step S31 is 5.

[0024] Furthermore, a preferred method is proposed, wherein step S32 comprises:

[0025] The heuristic Stein unbiased risk estimate is selected as the wavelet threshold;

[0026] Use the soft threshold function to set the coefficients less than the threshold to 0, retain the coefficients greater than the threshold, and subtract the threshold for processing.

[0027] Based on the same inventive concept, the present invention also proposes a system for suppressing ultra-wideband ranging random noise, the system comprising:

[0028] The data acquisition unit is used to acquire the original data of ultra-wideband ranging;

[0029] A decomposition unit, used for performing variational mode decomposition on the raw data of the ultra-wideband ranging;

[0030] The smoothing unit is used to process the decomposed data by adopting a wavelet threshold denoising method to obtain smooth ultra-wideband ranging data.

[0031] Based on the same inventive concept, the present invention also proposes a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes a method for suppressing ultra-wideband ranging random noise according to any one of the above.

[0032] Based on the same inventive concept, the present invention also proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a method for suppressing random noise in ultra-wideband ranging as described in any one of the above items are executed.

[0033] The present invention is beneficial in that:

[0034] The method proposed in the present invention aims at the problem that ultra-wideband signals have complex time-frequency characteristics and simple filtering methods cannot accurately retain useful signals. Through the decomposition and reconstruction process, the noise suppression is more accurate, thereby effectively improving the accuracy of ranging. Variational mode decomposition can effectively process the multi-scale characteristics of signals, adapt to the changes of different frequency components, and improve the processing ability of complex signals. Combined with wavelet threshold denoising technology, it can enhance the adaptability to different types of noise, effectively cope with the impact of environmental changes, and enhance the robustness of the system under different environmental conditions, especially in complex or dynamic environments.

[0035] The method proposed in the present invention decomposes the original data of ultra-wideband ranging based on the variational mode decomposition method: variational mode decomposition can adaptively capture different modes in the signal without setting basis functions. The variational mode decomposition method decomposes the original signal into multiple intrinsic mode functions, which reduces the mode aliasing phenomenon while separating noise interference and effective signals. Wavelet threshold denoising is used to filter the variational mode decomposition signal, and the multi-resolution analysis ability of wavelets is utilized to capture the local details and global features of each decomposed signal, fully strip the noise signal, and achieve efficient suppression of random noise in ultra-wideband ranging.

[0036] The present invention is applied to the field of indoor positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of a method for suppressing random noise in ultra-wideband ranging according to implementation mode 1;

[0038] Figure 2 is the time domain spectrum of the original signal according to the eleventh embodiment, wherein the horizontal axis represents time and the vertical axis represents amplitude;

[0039] Figure 3 This is a frequency spectrum comparison diagram of the original signal before and after noise reduction described in the eleventh implementation mode. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0041] Implementation method 1, see Figure 1 The present embodiment describes a method for suppressing random noise in ultra-wideband ranging, the method comprising:

[0042] S1: Get the raw data of ultra-wideband ranging;

[0043] S2: performing variational mode decomposition on the raw data of the ultra-wideband ranging;

[0044] S3: The decomposed data is processed using the wavelet threshold denoising method to obtain smooth ultra-wideband ranging data.

[0045] This implementation is aimed at the ultra-wideband ranging process. After being affected by the environment, the ultra-wideband ranging value will fluctuate greatly, affecting the ranging accuracy. Traditional denoising methods are difficult to effectively distinguish between signals and noise. The proposed method decomposes the original ultra-wideband ranging data into several modal functions, which respectively reflect the characteristics of the signal in different frequency ranges. In this way, noise can be effectively distinguished from signals.

[0046] Furthermore, considering that ultra-wideband signals have complex time-frequency characteristics, wavelet threshold denoising technology is applied to the decomposed modal signals to suppress noise at different scales. Wavelet transform can capture the local characteristics of the signal and eliminate noise by setting a threshold while retaining the original signal as much as possible. After denoising, the smoothed modal signal is reconstructed into the final ultra-wideband ranging data to improve measurement accuracy.

[0047] The method proposed in this embodiment effectively removes random noise to obtain smooth ranging data, thereby significantly improving the accuracy and reliability of ranging. The combination of variational mode decomposition and wavelet threshold denoising can better adapt to the processing of non-stationary signals and improve the adaptability of the algorithm to complex signals. The method is relatively fast in processing speed, suitable for real-time ranging applications, and can meet the needs of high-frequency sampling. This technology is not only suitable for ultra-wideband ranging, but can also be extended to other signal processing fields, such as radar, communications, and medical imaging.

[0048] Implementation method 2: This implementation method further limits the method for suppressing ultra-wideband ranging random noise described in implementation method 1, and step S2 includes:

[0049] S21: Assume that the spectrum of each mode is concentrated near the center frequency of the mode;

[0050] S22: Perform Hilbert transform on the single-sided spectrum of each mode in the raw data of ultra-wideband ranging to extract the instantaneous amplitude and phase;

[0051] S23: Use the exponential term to shift the spectrum of the mode to the baseband;

[0052] S24: Estimate the bandwidth of the baseband signal by Gaussian smoothing;

[0053] S25: Construct the optimization objective function and solve it using the augmented Lagrange multiplier method;

[0054] S26: using an alternating direction multiplier algorithm to iteratively solve each mode and its corresponding center frequency until a preset convergence condition is met, thereby obtaining a plurality of independent eigenmode functions.

[0055] In this embodiment, it is assumed that the spectrum of each mode is concentrated near its center frequency. This assumption provides a basis for subsequent modal decomposition, so that each mode can focus on a specific frequency feature. The Hilbert transform is used to extract the instantaneous amplitude and phase from the original data, which provides the necessary time-frequency information for analyzing the dynamic characteristics of the signal. After obtaining the instantaneous information, the change of the signal can be better understood, especially in a noisy environment. The modal spectrum is translated to the baseband using an exponential term so that signal processing can be performed in a more suitable frequency range. Baseband signals are generally easier to analyze and process, and the influence of high-frequency noise can be reduced. Estimating the bandwidth of the baseband signal by Gaussian smoothing helps to eliminate short-term fluctuations and obtain a smoother signal representation. The smoothed signal has better stability, which is convenient for subsequent analysis and processing, and reduces the influence of noise. Construct an optimization objective function and solve it by the augmented Lagrange multiplier method to more effectively process signals and constraints. The optimization process can find the optimal solution in a multi-constraint environment and improve the accuracy of modal extraction. The alternating direction multiplier algorithm is used to iteratively solve each mode and its center frequency until the convergence condition is met. The algorithm has good convergence and computational efficiency, is suitable for processing large-scale data, and helps to extract multiple independent intrinsic mode functions.

[0056] In this embodiment, by assuming the center of the spectrum and using steps such as Hilbert transform, the characteristics of each mode can be extracted with high precision to ensure the accuracy of the decomposition result. The augmented Lagrange multiplier method is combined with the alternating direction multiplier algorithm to improve the efficiency and stability of the optimization process, and the optimal solution can be found more quickly, reducing the consumption of computing resources. The design of this embodiment can adapt to the spectral characteristics of different signals, enhance the applicability and flexibility of the method in various environments, especially under complex background noise conditions. The intrinsic mode function obtained by decomposition can be analyzed separately, providing a clearer signal basis for subsequent wavelet denoising processing, and further improving the smoothness and accuracy of the overall ranging data. Through fine modal decomposition, random noise can be better identified and suppressed, making the final ranging data more reliable and suitable for real-time positioning applications.

[0057] Implementation method 3: This implementation method further limits the method for suppressing ultra-wideband ranging random noise described in implementation method 2, and step S25 includes:

[0058]

[0059] Among them, {u k (t)}={u1 (t),L,u K (t)} is the mode, {ω k}={ω 1 ,L,ω K} is the center frequency corresponding to the mode, α is the penalty factor of the quadratic term, λ is the Lagrangian operator, K is the total number of modes, δ(t) is the Dirac function, x(t) is the original signal, j is the imaginary unit, ω K is the center frequency of the kth modal component, and t is the time variable.

[0060] Embodiment 4: This embodiment further limits the method for suppressing ultra-wideband ranging random noise described in embodiment 1, and step S3 includes:

[0061] S31: Select an appropriate wavelet basis and decomposition layer number to perform wavelet decomposition on each intrinsic mode function to obtain wavelet coefficients;

[0062] S32: selecting a suitable threshold according to the noise characteristics of the original signal, and processing the wavelet coefficients at each scale according to the threshold;

[0063] S33: Perform inverse wavelet transform on the processed wavelet coefficients to reconstruct the denoised signal.

[0064] This embodiment effectively suppresses random noise in ultra-wideband ranging through a wavelet threshold denoising method. The principle is to decompose the signal by utilizing the multi-resolution characteristics of the wavelet transform. Specifically, by selecting an appropriate wavelet basis and the number of decomposition layers, the signal characteristics of different frequencies can be processed to extract effective information. At each scale, by setting a threshold according to the noise characteristics, unnecessary noise can be effectively removed and the main features of the signal can be retained. Finally, the signal is reconstructed through an inverse wavelet transform to obtain smooth ultra-wideband ranging data. The advantage of this method is that it is highly targeted and adaptable, and can improve ranging accuracy and signal quality in complex noise environments.

[0065] Implementation mode 5: This implementation mode further limits the method for suppressing random noise in ultra-wideband ranging described in implementation mode 4, and the wavelet basis in step S31 is the Db6 wavelet in the Daubechies wavelet.

[0066] In this embodiment, the Db6 wavelet has compact support and can capture the local characteristics and discontinuities of the signal; it has orthogonality and will not lose information during the wavelet transform and inverse transform process; it has good frequency resolution and can accurately capture the effective information of the low-frequency part of the signal.

[0067] Implementation method 6: This implementation method further limits the method for suppressing ultra-wideband ranging random noise described in implementation method 4, and the number of decomposition layers in step S31 is 5.

[0068] As the number of decomposition layers increases, the characteristic difference between the effective signal and the noise will gradually increase, which is conducive to the separation of the signal and the noise. However, for the wavelet transform algorithm, as the number of decomposition layers increases, the information lost in the step of signal decomposition and reconstruction will increase, which will lead to signal distortion. Therefore, in this embodiment, the number of decomposition layers is selected to be 5, so that the integrity of the reconstructed signal can be guaranteed while separating the noise as much as possible.

[0069] Embodiment 7: This embodiment further limits the method for suppressing ultra-wideband ranging random noise described in embodiment 4, and step S32 includes:

[0070] The heuristic Stein unbiased risk estimate is selected as the wavelet threshold;

[0071] Use the soft threshold function to set the coefficients less than the threshold to 0, retain the coefficients greater than the threshold, and subtract the threshold for processing.

[0072] In wavelet threshold denoising, the selection of threshold and threshold function is the most important. In the wavelet threshold denoising method, wavelet coefficients are generated after the signal is decomposed. For wavelet coefficients less than the threshold, they will be set to 0, which is the noise part in the signal. For the part greater than the threshold, it is retained and reconstructed to reconstruct the denoised signal. In this embodiment, the heuristic Stein unbiased risk estimation is selected as the wavelet threshold. This method is based on unbiased risk estimation and combines heuristic strategies to determine the threshold, so it can show good adaptability for signals with different noise levels.

[0073] Embodiment 8: A system for suppressing ultra-wideband ranging random noise according to this embodiment, the system includes:

[0074] The data acquisition unit is used to acquire the original data of ultra-wideband ranging;

[0075] A decomposition unit, used for performing variational mode decomposition on the raw data of the ultra-wideband ranging;

[0076] The smoothing unit is used to process the decomposed data by adopting a wavelet threshold denoising method to obtain smooth ultra-wideband ranging data.

[0077] Embodiment 9. A computer device described in this embodiment includes a memory and a processor, wherein a computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes a method for suppressing ultra-wideband ranging random noise according to any one of embodiments 1 to 7.

[0078] Embodiment 10. A computer-readable storage medium described in this embodiment, on which a computer program is stored, and when the computer program is executed by a processor, the steps of a method for suppressing ultra-wideband ranging random noise as described in any one of embodiments 1 to 7 are executed.

[0079] Implementation Method 11: See Figure 2 and Figure 3 This embodiment provides a specific example of a method for suppressing random noise in ultra-wideband ranging described in the first embodiment, and is also used to explain the second to seventh embodiments, specifically:

[0080] This implementation method aims at the indoor positioning environment and the requirements of ultra-wideband high-precision positioning applications, and proposes a method for suppressing random noise in ultra-wideband ranging. In this method, the original data of ultra-wideband ranging is first processed, and the ranging data is decomposed by the variational mode decomposition method. In this step, the random noise and valid data in the original data are separated, and then the wavelet threshold denoising method is used to process the decomposed data to further suppress random noise. In order to obtain smooth ultra-wideband ranging data, it is used for ultra-wideband positioning solution. In this way, the accuracy of ultra-wideband indoor positioning can be improved.

[0081] Specifically, the original data of ultra-wideband ranging is first decomposed to separate the interference signal and the effective signal. The ultra-wideband signal is affected by the transmission environment and contains a variety of noise. It is decomposed into multiple sub-signals through variational mode decomposition. These sub-signals are called intrinsic mode functions. For each intrinsic mode function, it can be written as:

[0082] u k (t) = a k (t)cos(φ k (t)) (1)

[0083] Among them, a k (t) is the instantaneous amplitude of the kth mode, φ k (t) is the phase function, and the derivative of the phase function ω k (t) = φ′ k (t) represents the frequency. For the original signal, it can be expressed as the sum of multiple eigenmode functions:

[0084]

[0085] In order to decompose the original signal into multiple intrinsic mode functions, the variational mode decomposition method adopts the assumption that for any mode, its spectrum is concentrated near its center frequency. Therefore, the optimization objective function can be constructed. First, the single-sided spectrum of each mode is solved by Hilbert transform, then the spectrum of the mode is translated to the baseband using the exponential term, and finally the bandwidth of the baseband signal is estimated using Gaussian smooth transition. The above process can be written as:

[0086]

[0087] Among them, {u k (t)}={u 1 (t),L,u K (t)} and {ω k}={ω 1 ,L,ω K} are the modes and their corresponding center frequencies, and then the augmented Lagrange multiplier method is used to solve the optimization problem.

[0088]

[0089] Among them, α is the penalty factor of the quadratic term, λ is the Lagrangian operator, and K is the total number of modes. Then, each mode and its corresponding center frequency are obtained by the alternating direction multiplier algorithm, and the formula is as follows:

[0090]

[0091]

[0092]

[0093] Finally, repeat the above process until the equation is satisfied:

[0094]

[0095] Through the above steps, the original signal of the ultra-wideband signal is decomposed by the variational mode decomposition method, and it is decomposed into multiple independent intrinsic mode functions, which preliminarily separates the noise and the effective signal.

[0096] Furthermore, the wavelet threshold denoising method is used to process each intrinsic mode function after decomposition to remove the noise. Assume that the noisy signal is:

[0097] f(n)=s(n)+u(n) (9)

[0098] Among them, s(n) is the original signal, and u(n) is the noise signal. The wavelet transform of this signal is:

[0099] WT f (a, b)=WT s (a, b)+WT u (a, b) (10)

[0100] Wavelet threshold denoising performs wavelet transform on the noisy signal, performs multi-scale analysis in the wavelet domain, retains the effective signal, and suppresses the noise signal. Finally, the retained effective signal is inversely transformed in the wavelet domain to reconstruct the signal. The signal at this time is the denoised signal.

[0101] The process of wavelet threshold denoising is as follows:

[0102] 1. Determine the wavelet function and its decomposition layers, and complete the wavelet decomposition;

[0103] 2. Select a suitable threshold according to the characteristics of the original signal, such as the frequency and amplitude of the signal compared to the noise, and process the wavelet coefficients at each scale according to the threshold;

[0104] 3. Perform inverse wavelet transform on the retained wavelet coefficients to reconstruct the signal. The signal at this time is the denoised signal.

[0105] According to the wavelet threshold denoising theory, the factors that determine the denoising performance mainly include: (1) wavelet basis; (2) the number of wavelet decomposition layers; (3) threshold; (4) threshold processing function. This implementation method mainly filters the original data of ultra-wideband ranging and suppresses the random noise in the data. Therefore, the effective part is the low-frequency part of the signal, and the noise is the high-frequency part of the signal.

[0106] Therefore, in this embodiment, the wavelet base used is the Daubechies wavelet, which has tight support, helps to achieve signal localization, and can capture the local characteristics and discontinuities of the signal; it has orthogonality, which can ensure that no information is lost during the wavelet transform and inverse transform process; it has good frequency resolution, and can accurately capture the low-frequency characteristics of the signal. The wavelet base has the characteristic of variable support length, which is usually expressed in the form of DbN, where N represents the support length. The present invention selects the Db6 wavelet, whose support length is 6. Db6 is a high-order wavelet with better smoothness and frequency resolution, and can accurately grasp the changing trend of effective signals in ultra-wideband ranging data.

[0107] As the number of decomposition layers increases, the characteristic difference between the effective signal and the noise will gradually increase, which is conducive to the separation of the signal and the noise. However, for the wavelet transform algorithm, as the number of decomposition layers increases, the information lost in the step of signal decomposition and reconstruction will increase, which will lead to signal distortion. Therefore, in this embodiment, the number of decomposition layers is selected to be 5, so that the integrity of the reconstructed signal can be guaranteed while separating the noise as much as possible.

[0108] In wavelet threshold denoising, the selection of threshold and threshold function is the most important. In the wavelet threshold denoising method, wavelet coefficients are generated after the signal is decomposed. For wavelet coefficients less than the threshold, they will be set to 0, which is the noise part in the signal. For the part greater than the threshold, it is retained and reconstructed to reconstruct the denoised signal. In this embodiment, the heuristic Stein's unbiased risk estimate is selected as the wavelet threshold. This method is based on unbiased risk estimation and combines heuristic strategies to determine the threshold, so it can show good adaptability for signals with different noise levels.

[0109] As for the threshold function, it is generally divided into a soft threshold function and a hard threshold function. The hard threshold function sets all the parts less than a given threshold to 0, and the rest remain unchanged. This may cause a ringing effect. The soft threshold, on the other hand, sets the part less than the threshold to 0 and subtracts the threshold from the rest, which makes it have better continuity and a smoother denoising effect. Therefore, the soft threshold is selected in the present invention.

[0110] See also Figure 2 and Figure 3 As shown in Figure 1, the wavelet threshold denoising method can be used to independently denoise each intrinsic mode function after variational mode decomposition. This operation effectively removes the noise of various frequency components in the original signal, improves the overall denoising effect, and achieves higher ultra-wideband indoor positioning accuracy.

[0111] In summary, the present invention proposes a variational mode decomposition and wavelet threshold denoising method for suppressing random noise in ultra-wideband ranging. The method first uses the variational mode decomposition method to decompose the original data of ultra-wideband ranging into multiple intrinsic mode functions according to the center frequency. This step separates the noise and the effective signal in the original data. Then, the wavelet threshold denoising method is used to filter the decomposed signal. In this step, the wavelet threshold denoising filters each intrinsic mode function in the decomposed signal. The noise in various frequencies can be fully suppressed, and the overall random noise suppression effect is improved. Finally, the retained wavelet coefficients are reconstructed by wavelet inverse transformation, and a denoised signal is generated.

[0112] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure rather than to limit its protection scope. Although the present disclosure has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the present disclosure, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the disclosed claims to be approved.

Claims

1. A method for suppressing random noise in ultra-wideband ranging, characterized in that: The method comprises: S1: Get the raw data of ultra-wideband ranging; S2: performing variational mode decomposition on the raw data of the ultra-wideband ranging; S3: The decomposed data is processed using the wavelet threshold denoising method to obtain smooth ultra-wideband ranging data.

2. The method for suppressing random noise in ultra-wideband ranging according to claim 1, characterized in that: The step S2 comprises: S21: Assume that the spectrum of each mode is concentrated near the center frequency of the mode; S22: Perform Hilbert transform on the single-sided spectrum of each mode in the raw data of ultra-wideband ranging to extract the instantaneous amplitude and phase; S23: Use the exponential term to shift the spectrum of the mode to the baseband; S24: Estimate the bandwidth of the baseband signal by Gaussian smoothing; S25: Construct the optimization objective function and solve it using the augmented Lagrange multiplier method; S26: using an alternating direction multiplier algorithm to iteratively solve each mode and its corresponding center frequency until a preset convergence condition is met, thereby obtaining a plurality of independent eigenmode functions.

3. The method for suppressing random noise in ultra-wideband ranging according to claim 2, characterized in that: The step S25 comprises: Among them, {u k (t)}={u1(t),L,u K (t)} is the mode, {ω k }={ω1,L,ω K } is the center frequency corresponding to the mode, α is the penalty factor of the quadratic term, λ is the Lagrangian operator, K is the total number of modes, δ(t) is the Dirac function, x(t) is the original signal, j is the imaginary unit, ω K is the center frequency of the kth modal component, and t is the time variable.

4. The method for suppressing random noise in ultra-wideband ranging according to claim 1, characterized in that: The step S3 comprises: S31: Select an appropriate wavelet basis and decomposition layer number to perform wavelet decomposition on each intrinsic mode function to obtain wavelet coefficients; S32: selecting a suitable threshold according to the noise characteristics of the original signal, and processing the wavelet coefficients at each scale according to the threshold; S33: Perform inverse wavelet transform on the processed wavelet coefficients to reconstruct the denoised signal.

5. The method for suppressing random noise in ultra-wideband ranging according to claim 4, characterized in that: The wavelet basis in step S31 is the Db6 wavelet in the Daubechies wavelet.

6. The method for suppressing random noise in ultra-wideband ranging according to claim 4, characterized in that: The number of decomposition levels in step S31 is 5.

7. The method for suppressing random noise in ultra-wideband ranging according to claim 4, characterized in that: The step S32 comprises: The heuristic Stein unbiased risk estimate is selected as the wavelet threshold; Use the soft threshold function to set the coefficients less than the threshold to 0, retain the coefficients greater than the threshold, and subtract the threshold for processing.

8. A system for suppressing random noise in ultra-wideband ranging, characterized in that: The system comprises: The data acquisition unit is used to acquire the original data of ultra-wideband ranging; A decomposition unit, used for performing variational mode decomposition on the raw data of the ultra-wideband ranging; The smoothing unit is used to process the decomposed data by adopting a wavelet threshold denoising method to obtain smooth ultra-wideband ranging data.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the processor runs the computer program stored in the memory, the processor executes a method for suppressing random noise in ultra-wideband ranging according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of a method for suppressing random noise in ultra-wideband ranging as claimed in any one of claims 1 to 7 are executed.

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